Virtual testing model for use in simulated aerodynamic testing

ABSTRACT

A method for developing a virtual testing model of a subject for use in simulated aerodynamic testing comprises providing a computer generated generic 3D mesh of the subject, identifying a dimension of the subject and at least one reference point on the subject, imaging the subject to develop point cloud data representing at least the subject&#39;s outer surface and adapting the generic 3D mesh to the subject. The generic 3D mesh is adapted by modifying it to have a corresponding dimension and at least one corresponding reference point, and applying at least a portion of the point cloud data from the imaged subject&#39;s outer surface at selected locations to scale the generic 3D mesh to correspond to the subject, thereby developing the virtual testing model specific to the subject.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/197,119, filed Mar. 4, 2014, now U.S. Pat. No. 9,797,802, whichclaims the benefit of U.S. Provisional Patent Application No. 61/772,464filed on Mar. 4, 2013, both of which are incorporated herein byreference.

BACKGROUND

Aerodynamic testing is important for athletes, particularly in sportswhere results are decided by fractions of a second, such as in cycling,skiing, ski jumping, skating and bobsledding, as just a few examples.

Conventionally, aerodynamic testing has included sessions in a windtunnel facility to generate conditions that are as close to real-worldconditions as possible. In this way, athletes can evaluate differentfactors, including body positions and clothing and equipment choices, todetermine which configuration should produce less aerodynamic drag, andthus be faster on the race course.

Wind-tunnel testing opportunities for athletes, however, are limitedbecause wind tunnel testing time is very expensive. Moreover, it isdifficult to make changes to a configuration and then meaningfullyevaluate the changes in real-time during a single tunnel testingsession.

SUMMARY

Described below are systems and methods that address drawbacks ofconventional technology used for aerodynamic testing. These systems andmethods are particularly suited to testing athletes and their clothingand equipment, but the same principles can be applied to other areas.

According to one implementation, a method for developing a virtualtesting model of a subject for use in simulated aerodynamic testingincludes providing a computer generated generic 3D mesh of the subject,identifying a dimension of the subject and at least one reference pointon the subject, imaging the subject to develop point cloud datarepresenting at least the subject's outer surface and adapting thegeneric 3D mesh to the subject by modifying it to have a correspondingdimension and at least one corresponding reference point. Thecorresponding dimension corresponds to the identified dimension of thesubject and corresponding reference point corresponds to the identifiedat least one reference point of the subject. The generic 3D mesh ismodified further by applying at least a portion of the point cloud datafrom the imaged subject's outer surface at selected locations to scalethe generic 3D mesh to correspond to the subject, thereby developing thevirtual testing model specific to the subject.

Providing a computer generated generic 3D mesh of the subject caninclude subjecting the subject to a motion capture process, and the actof imaging the subject to develop point cloud data can occur concurrentwith the motion capture process.

Imaging the subject can include using at least one of laser scanning,stereo vision, and optical high resolution 3D scanning techniques.

In one implementation, the subject is a human subject, the dimension ofthe subject is the human subject's height, and the at least onereference point is taken from a set of reference points establishing thehuman subject's limb lengths and elbow, knee and ankle positions.

In one implementation, as an example, modifying the 3D mesh furtherincludes scaling the generic 3D mesh of the human subject to correspondto the human subject's actual lower leg circumference by applying pointcloud data representing the lower leg circumference, the point clouddata having a common datum with the generic 3D mesh.

In some implementations, modifying the generic 3D mesh further byapplying point cloud data occurs substantially in real time. In someimplementations, developing the virtual testing model specific to thesubject occurs without filling holes and without resolvingdiscontinuities present in the point cloud data representing thesubject's outer surface.

In some implementations, the subject is a human subject, the humansubject is recorded during a motion capture session emulating anactivity, and wherein, concurrent with the motion capture session, theimaging of the subject is completed, thereby allowing the adapting ofthe generic 3D mesh to be completed substantially in real time todevelop the virtual testing model of the human subject in motion.

In some implementations, the approach further comprises animating thevirtual testing model based on motion capture data of the subject inmotion and subjecting the animated virtual testing model to simulatedaerodynamic testing. In some implementations, the approach comprisessupplementing the animated virtual testing model of the human subjectwith an accessory submodel representing at least one of an article ofclothing worn by the human subject or an item of equipment used by thehuman subject. As just some examples, the accessory submodel cancomprise a submodel for a garment wearable by the human subject, ahelmet wearable by the human subject and/or a bicycle to be ridden bythe human subject.

In some implementations, the animated virtual testing model isdisplayed, and the approach includes dynamically evaluating changes tothe human subject based on changes to the displayed animated virtualtesting model.

In some implementations, the subject is a human subject, the humansubject is recorded during a motion capture session, and, concurrentwith the motion capture session, the human subject is imaged. Further,the generic mesh is adapted by correlating thin segments of point clouddata along a desired axis to corresponding locations in the generic meshand applying valid point cloud data from the segments to selectivelyresize the generic mesh to more closely match the human subject, and, ifno valid point cloud data exists for a specific segment, then dimensionsof the generic mesh at a location corresponding the specific segment areretained.

In another implementation, a method for developing a virtual testingmodel of a human subject in motion for use in simulated aerodynamictesting comprises recording the human subject in motion in a motioncapture session using a generic 3D mesh of the human subject. Concurrentwith the motion capture session, at least a portion of the human subjectis imaged by developing point cloud data of the human subject.Substantially in real time, the generic 3D mesh is adapted to match thesubject more closely at locations where valid point cloud data ispresent by modifying the generic 3D mesh at corresponding locations tohave dimensions matching the point cloud data.

Associated computer program products that implement all or parts of theabove methods are also described.

According to another implementation, a method of simulating aerodynamictesting, comprises providing a computer-generated model of a humansubject suitable for computational fluid dynamics analysis, providing astereo image of the human subject, and mapping the computer-generatedmodel and the stereo image together to develop a drag weight per pixelof the image. The drag weight for each individual pixel of the stereoimage can be summed to determine an overall drag weight. A change indrag weight can be calculated from evaluating a change in the stereoimage.

According to another implementation, first and second drag weights atrespective first and second yaw angles are established by computationalfluid dynamics calculations; and intermediate yaw angles between firstand second yaw angles are determined by interpolating between the firstand second weights and using the calculated drag weight per pixel todetermine corresponding drag weights.

These and other implementations are more fully described below inconnection with the following drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a portion of a screen shot from a software application runningon a computer that shows a representation of a human subject with ageneric 3D mesh.

FIG. 2 is a representation of the FIG. 1 subject, such as might be shownin a screen shot, in the process of undergoing a high resolution imagescan to develop a point cloud data representation of his outer surface.

FIG. 3 is a representation of the subject from FIG. 1 and FIG. 2 asdeveloped into a virtual testing model of the human subject.

FIG. 4 is a flow chart of a method for developing a virtual testingmodel as described in connection with FIGS. 1-3.

FIG. 5 is a screen shot showing a generic 3D mesh of a human subject ina motion capture modelling environment.

FIG. 6 is a screen shot taken from the same modelling environment asFIG. 5 but showing the generic 3D mesh of the human subject turned offto show the points of the subject's body found during the modellingprocess.

FIG. 7 is a screen shot taken from the same modelling environment asFIGS. 5 and 6 but showing a solid surface form of the generic 3D mesh.

FIG. 8 is a screen shot from an image scanning application showing ascanned point cloud image of the human subject of FIGS. 5-7.

FIG. 9 is a screen shot taken from a 3D modelling and animationapplication showing a completed virtual testing model of the humansubject as developed from FIGS. 5-8.

FIG. 10 is a screen shot taken from a modeling program that illustratesthe underlying surface of the model of FIG. 9.

FIG. 11 is a screen shot from a modeling program showing the virtualtesting model developed from FIGS. 5-8 being subjected to simulatedaerodynamic testing with computational fluid dynamics.

FIG. 12 is a flow chart showing steps of a representative method fordeveloping a virtual testing model.

FIG. 13 is a screen shot showing a representative image scan of abicycle.

FIG. 14 is a screen shot showing a model of the bicycle of FIG. 12 asdeveloped for aerodynamic testing.

FIG. 15 is a screen shot of the model of the bicycle of FIG. 13undergoing aerodynamic testing using computational fluid dynamics.

FIG. 16 is a front elevation view of a human subject in a T pose andwearing instrumentation for generating desired position data.

FIG. 17 is an elevation view of a human subject in a standing positionand wearing a helmet during an image scanning operation.

FIG. 18 is a side elevation view showing a human subject riding abicycle supported in a stationary position to allow dynamics of movinglegs, wheels and bicycle drivetrain to be modelled.

FIG. 19 is a flow chart of a method of conducting image scanningconcurrent with the motion phase.

FIGS. 20A and 20B are elevation views from two different angles of ahuman subject on a bicycle with a superimposed grid to allow calculationof drag according to a new pixel mapping approach.

DETAILED DESCRIPTION

With reference to the flow chart of FIG. 4 and FIGS. 1, 2 and 3, arepresentative method 100 and corresponding systems for developing avirtual testing model of a human subject (also sometimes referred toherein as the athlete) for use in aerodynamic testing can be described.

In step 110, a computer-generated 3D mesh of a human body form isprovided. In the example of FIG. 1, which is taken from a computersoftware modelling environment, a generic 3D polygon mesh in human bodyform is depicted standing erect with arms slightly outstretched.

In step 112, the generic 3D mesh (also sometimes referred to herein asthe avatar) is adapted to the human subject. Because the mesh isgeneric, it must be sized or fitted to the actual subject. Thus, thegeneric 3D mesh is modified to have the actual height and at least oneother body reference point of the human subject.

According to one approach, a T Pose program is used, and the genericmesh is modified to have the human subject's height, limb lengths,elbow, knee and ankle positions. It would be possible, of course, to usefewer or more body reference points. The key is use an appropriatenumber to allow an accurate intermediate representation to be made.Alternatively, as is described below in greater detail, the subject'sheight and one or more reference points can be obtained from a motioncapture phase.

In step 114, the human subject is imaged, and point cloud datarepresenting the human subject's outer surface is developed. FIG. 2shows a representative point cloud data model from an image scan of thehuman subject's outer surface while in a position corresponding to the3D mesh of FIG. 1.

In step 116, the generic 3D mesh from step 112 is further modified byapplying point cloud data from step 114. Specifically, point cloud datafrom the imaged human subject's outer surface at selected locations isapplied to scale the generic 3D mesh at corresponding locations. In thisway, a virtual testing model specific to the human subject is developed(step 118).

With reference to FIGS. 1-3, point cloud data for selected locations(see examples shown at the horizontal bands 12, 14 at mid-torso andlower leg levels) from FIG. 2 is applied to the generic 3D mesh of FIG.1 to develop the virtual testing model shown schematically in FIG. 3.Using the lower right leg as one illustrative example, it can be assumedthat the locations of the human subject's knees and ankles are specifiedin the generic 3D mesh of FIG. 1, but the cross-sectional size of thehuman subject's lower right leg is not known, so the generic 3D meshuses a generic dimension at the indicated elevation. The point clouddata for the human subject's lower right leg at the same elevation canbe applied to make the generic 3D mesh specific to the human subject atthat elevation. These steps can be carried out in a continuous fashionover the height of the human subject to “fill in” missing data,including at levels above, below and between the finite body referencepoints of the generic 3D mesh.

In specific implementations, adapting the generic 3D mesh to the humansubject and the imaging of the human subject take place concurrently.The imaging can be completed using laser scanning, stereo vision,optical high resolution 3D scanning or other similar techniques. In theillustrated example, the approach leverages the fact that the subject isstanding erect and thus the ground is a common datum for modifying thegeneric 3D mesh and for imaging the human subject.

It should be noted that although the human subject is scanned to developpoint cloud data representing the human subject's outer surface, suchdata is incomplete and will have discontinuities. For example, there aretypically “holes” in the data at the locations of the human subject'seyes, mouth and crotch, to name a few examples. To use the imaged humansubject's outer surface in computational fluid dynamics (CFD), these andother holes in the data and discontinuities would first need to beresolved. Conventionally, this requires hours of a skilled user's timeto “shape” the data and “fill” the holes. A resulting complete surfacescan may have on the order of 500,000 data points, in contrast to theapproach taken here where the virtual testing model may have on theorder of 10,000 data points, i.e., well less than 10% and even as few as2% as the complete surface scan.

In addition, modifying the generic 3D mesh by applying point cloud datato develop the virtual testing model occurs substantially in real-time.In contrast to conventional approaches that may require hours ofprocessing time, the described approaches are completed in minutes, ifnot in seconds, for most typical test subjects. As a result, thedescribed approaches allow the virtual testing model to be developed(step 118) and subjected to simulated aerodynamic testing usingcomputational fluid dynamics (step 120) substantially in real time,i.e., in a stepwise fashion without lengthy delays or idle times fordata processing.

In some implementations, the human subject is in motion, such asemulating a selected activity, while the human subject is being imaged.For example, the human subject can be riding a bicycle set to bestationary but allowing the subject to turn the pedals and cause atleast the rear wheel to rotate. Imaging data of the human subject inmotion can be used to animate the generic 3D mesh of the human subject,thus allowing dynamic modeling of the human subject in motion.

FIGS. 5-11 illustrate an implementation of the above method as appliedto a human subject or athlete being modelled in his position for ridinga bicycle. In FIG. 5, a screen capture or screen shot of a generic 3Dmesh 200 of a human subject is shown in a main portion of the screen. Ascan be seen, the generic 3D mesh 200 is shown with suggestive skeletalelements and data points. In the implementation of FIG. 5, the datapoints have been developed from a motion capture session in which ahuman subject is riding a bicycle while the subject's motion is capturedwith one or more motion capture cameras. One suitable motion capturesystem for recording the human subject is the OptiTrack Motive softwareavailable from NaturalPoint, Inc. and suitable cameras. FIG. 18 shows anexample of a human subject riding a bicycle that is secured to a deviceto hold it stationary.

In FIG. 6, the skeletal elements overlay has been turned off, whichreveals more of the data points corresponding to joint locations on thehuman subject. As just two examples, the human subject's ankles areshown at 210, and his knees are shown at 212. The positions of theankles 210 and knees 212 are established according to well-known motioncapture principles involving the use of high speed filming (such as at120 frames per second or faster). Motion capture techniques usingmarkers placed on the human subject's body or so-called markerlesstracking techniques can be used. In FIG. 7, the generic 3D mesh is shownhaving a more solid form, and can be shown in motion corresponding tothe motion being captured.

FIG. 8 is a screen shot from a solid modelling application displaying ascanned point cloud image 216 of a side elevation of the human subjectof FIGS. 5-7, i.e., as positioned while riding a bicycle. As can beseen, there are holes and discontinuities in the point cloud data, suchas in the area of the human subject's arm pit. One suitable imagescanning system for obtaining the point cloud data of the human subjectis one or more Arctec Eva scanners. The solid modelling application usedto display and manipulate the scanned point cloud image 216 in FIG. 8 isSolidWorks 3D CAD, but any other similar modelling program could also beused.

FIG. 9 is a screen shot showing a virtual model 220 of the human subjectdeveloped from FIGS. 5-7 (now shown as clothed for cycling) after havingbeen adapted based on the point cloud data of FIG. 8. Specifically,segments from the point cloud data, where present and valid, have beenused to resize the generic 3D mesh according to the human subject'sactual dimensions. FIG. 10 is another view of the virtual model 220 ofthe human subject, which is shown in a transparent format to reveal thecompleteness of the model.

FIG. 11 is a screen shot showing the virtual model 220 of the humansubject in position on a bicycle and being subjected to simulatedaerodynamic testing using computational fluid dynamics. As shown, theflow lines and aerodynamic forces can be shown in relation to the humansubject and the bicycle to convey a realistic illustration of thephysical environment and improve comprehension. Suitable softwareenvironments for displaying and manipulating models with CFD includeSolidWorks Flow and ANSYS Fluent. The virtual model 220 is a moreaccurate representation and provides faster and more meaningful resultsin a more cost-effective manner than conventional approaches.

FIG. 12 is a flow chart of one representative method for developing avirtual model. In step 1310, a motion capture session is initiated, suchas is described above in connection with FIGS. 5-7, using a generic 3Dmesh of the subject. For example, a generic mesh of a human form can beused in cases where the subject is human. It should be noted that othertypes of subjects, such as objects (including clothing, equipment andother types of objects), can be subjected to motion capture and relatedmodeling along the same lines.

In step 1312, a dimension of the subject is selected. For example, inthe case of a human subject, the dimension can be the vertical dimensionor the subject's height. This dimension then defines an axis used insubsequent steps. In step 1314, the subject is divided into segments (or“slices”) along the axis from a base of the subject to the subject'soutermost extent along the axis. Referring to FIG. 5 as one example, thesubject's feet can be regarded as the base, and the axis can extend fromthe base in a vertical direction to a highest elevation of the generic3D mesh. Each segment or slice can be about 0.1 mm to about 1.0 mm, andtypically about 0.5 mm. Segments as thin as 0.01 mm are achievable withavailable high resolution motion capture cameras, but very long CFDprocessing times must then be addressed.

In step 1315, point cloud data of the human subject is obtained. Asdescribed elsewhere, such point cloud data is advantageously obtainedconcurrent with motion capture of the human subject. It is also possibleis some situations, however, to use previously obtained point clouddata.

In step 1316, a routine is begun that calls for the point cloud data tobe accessed at a position corresponding to a current slice or segment inthe generic 3D mesh. For example, this routine can be assumed to startat the base and to move along the axis. In the example of FIG. 5, thefirst slice can be at the feet of the human subject. In step 1318, theroutine checks if point cloud data exists for a given slice in thegeneric mesh. In addition, the program determines if the data ismeaningful. If the point cloud data does correspond to the generic 3Dmesh, i.e., if it is close enough to a predicted location on the genericmesh to suggest that it is valid data, then the generic mesh is modified(“pushed” or “pulled”) so that its outer surface has the dimension ofthe point cloud data (step 1322). If there is no point cloud data forany particular slice or if the point cloud data is suspect (e.g., toofar from the position predicted by the generic mesh), then the pointcloud data is disregarded (step 1320) and the generic mesh data isretained for that slice. In step 1324, the routine checks to see if thelast slice has been processed. If not, the process moves to the nextslice (step 1326) and returns to step 1316. If the last slice has beenprocessed, then the routine is ended. Even using slices or segments of0.5 mm, the process can be completed in seconds or minutes for mosttypical scenarios.

As a result, the completed virtual model is specific to the humansubject and has the dimensions provided by the point cloud data atappropriate locations, but also has reasonable dimensions provided bythe generic mesh in areas where the point cloud data may have beendeficient, and thus does not have any holes. Such holes in the resultingdata would require time-consuming and difficult modification throughmanual operations.

The modeling of the human subject is typically supplemented withsubmodels representing clothing worn by the human subject or equipmentused by the subject during the modeled activity. For cycling as oneexample, submodels can represent the subject's position on the bicycle,the bicycle, the subject's clothing and/or the subject's helmet. In thisway, changes to any of these attributes can be measured, e.g., todetermine a change in aerodynamic drag. New configurations, such as thesame subject but with a different helmet or different bicycle can berelatively quickly simulated without the high cost and delay of actualwind tunnel testing. In addition to simulated aerodynamic testing, thevirtual model in conjunction with one or more equipment or clothingsubmodels can be used to evaluate fit and interoperability of theequipment and/or clothing with the human subject.

Described below are additional drawing figures. FIG. 13 is a screendisplay showing a representative image scan of a bicycle. FIG. 14 is acorresponding model of the bicycle of FIG. 13 developed for aerodynamictesting. It can be seen that some of the bicycle components, such as thewheels, the frame and the crankset, as just three examples, aresimplified in the model of FIG. 14 as compared to the imaged bicycle ofFIG. 13. FIG. 15 is a screen display of the bicycle model of FIG. 14during testing with representations of the magnitude and direction ofdrag forces. FIG. 16 illustrates a human subject in the T pose positionand with markers at selected reference points. FIG. 17 illustrates ahuman subject in a standing position undergoing imaging of his outersurface, such as by two stereo cameras. FIG. 18 illustrates the humansubject riding a bicycle in place on a test stand to allow the dynamicsof the rotating wheels and crankset, as well as the subject's movinglegs and other motion, to be modeled.

FIG. 19 is a flow chart of a method in which image scanning occursconcurrent with the motion capture phase. In step 1400, the motioncapture phase as described above is initiated. At about the same time,and in parallel with the motion capture phase, imaging scanning of thesubject is also initiated. According to one embodiment, there aremultiple motion capture cameras or other devices arranged in a scene tocapture the motion of one or more subjects.

By reassigning at least two of the cameras/devices for image scanning(step 1420), such as by configuring cameras in a video mode, then imagescanning can be conducted concurrent with the motion capture phase. Thetwo cameras/devices designated for scanning should be arranged relativeto each other to develop an appropriate scanned image of the subject. Instep 1430, motion capture of the subject is being conducted in parallelwith image scanning of the subject. For example, there is a time stampassociated with each capture device (e.g., stereo camera, Artec EVA,LASER scanner or other such device) that allows the various frames to behandled digitally and synchronized as desired. If devices have differentcapture rates, then the rate of the slowest device is selected tocontrol the building of the mesh and the faster devices are averaged tothe rate of the slowest device or intermediate frames on the fasterdevices are simply dropped, thereby maintaining the devices insynchronization with each other. Alternately the devices may be genlocked by the central computer or chip such that all devices fire at adesignated rate, thus assuring parallel processing. As described above,in step 1440, any available point cloud data that is valid is assignedto the generic mesh to allow it to be resized and made more accuraterelative to the human subject.

Among the benefits of the described approaches are the following: (1)the human subject's motion can be captured—and an image scan of hisouter surface can be completed—in a single sequence of motion withoutbreaks using one or more articles of equipment (e.g., bicycle, helmet,skinsuit, etc.) for accurate modelling; (2) the human subject's smallmovements during the motion capture phase do not cause errors inmodelling because the in above approaches the overall mesh is alwaysdefined, and thus defined surface movement is recognized as a 3Dtranslation of the surface and not noise; (3) the image scan of the 3Dsurface can be prepared concurrent with the motion capture phase, sothere is no lengthy delay in deriving a stitched together image; and (4)the virtual model is complete because regions of the scanned image inwhich there holes or other discontinuities are not used (rather, thedata from the generic 3D mesh is retained in these areas).

A related modeling approach as shown generally in FIGS. 20A and 20B isreferred to as dynamic pixel mapping. FIGS. 20A and 20B show elevationsof a human subject riding a bicycle and a superimposed grid pattern.Pixel mapping is a relatively crude approach to measuring drag thatinvolves assigning each element of a cross section that faces the airflow with a similar weighting. Conventional pixel mapping assumes thateach occupied pixel faces the air flow orthogonally, i.e., that theentire surface under consideration is flat, but at least for surfaces atthe boundaries of a bicycle or a body, the occupied pixels are notoriented orthogonally. Therefore, these boundary pixels should have areduced weighting because they contribute less drag. By using stereoimaging technology, the weighting for pixels throughout a non-flat crosssection can be measured more accurately, thereby allowing a more precisecalculation of drag that accounts for boundary pixels.

Mapping the CFD elements incorporating differential drag grams to thestereo pixel map enables assigning every pixel of the stereo image adifferential drag gram weight. This measure of drag weight in grams perpixel may then be used independent of the CFD analysis to calculate thedrag weight in grams. The per pixel drag weight may also be summed tocalculate the complete drag weight from the pixel map rapidly at theframe rate of the stereo vision motion capture imaging device to givedynamic drag grams. Rapid calculation of drag grams may be used tointerpolate between CFD calculations that require lengthy computercomputations, which further enhances a dynamic calculation.

In view of the many possible embodiments to which the disclosedprinciples may be applied, it should be recognized that the illustratedembodiments are only preferred examples and should not be taken aslimiting in scope. Rather, the scope of protection is defined by thefollowing claims. I therefore claim all that comes within the scope andspirit of these claims.

What is claimed is:
 1. A method of simulating aerodynamic testing,comprising: providing a computer-generated model of a human subjectsuitable for computational fluid dynamics analysis; providing a stereoimage of the human subject; mapping the computer-generated model and thestereo image together to develop a drag weight per pixel of the image.2. The method of claim 1, wherein a drag weight for each individualpixel of the stereo image is summed to determine an overall drag weight.3. The method of claim 1, where a change in drag weight is calculatedfrom evaluating a change in the stereo image.
 4. The method of claim 1,wherein first and second drag weights at respective first and second yawangles are established by computational fluid dynamics calculations; andwherein intermediate yaw angles between the first and second yaw anglesare determined by interpolating between the first and second weights andusing the calculated drag weight per pixel to determine correspondingdrag weights.
 5. A method for developing a virtual testing model of asubject for use in simulated aerodynamic testing using one or morecomputers, comprising: using a computer to generate a simulationenvironment in which a generic 3D mesh of the subject is calculated;receiving an input via the computer identifying a dimension of thesubject and at least one reference point on the subject; receiving aninput via the computer of point cloud data developed from imaging thesubject with an image scanner and representing at least the subject'souter surface in the simulation environment; using the computer to adaptthe generic 3D mesh to the subject by modifying the generic 3D mesh tohave a corresponding dimension and at least one corresponding referencepoint, the corresponding dimension being calculated or numericallysampled to correspond to the identified dimension of the subject and theat least one corresponding reference point being calculated ornumerically sampled to correspond to the identified at least onereference point of the subject; and using the computer to modify thegeneric 3D mesh further by applying at least a portion of the pointcloud data from the imaged subject's outer surface at selected locationsto scale the generic 3D mesh to have the subject's actual circumferenceat corresponding locations, thereby developing the virtual testing modelspecific to the subject and suitable for computational fluid dynamicsanalysis.
 6. The method of claim 5, further comprising receiving acomputer input of motion capture data from a motion capture process ofthe subject and mapping the virtual testing model and the imaging datatogether.
 7. The method of claim 5, wherein the motion capture processis completed concurrent with the imaging of the subject with the imagescanner.
 8. The method of claim 5, wherein the point cloud data isdeveloped from imaging the subject using at least one of laser scanning,stereo vision, and optical high resolution 3D scanning techniques. 9.The method of claim 5, wherein the subject is a human subject, thedimension of the subject is the human subject's height, and the at leastone reference point is taken from a set of reference points establishingthe human subject's limb lengths and at least one of an elbow position,knee position and ankle position.
 10. The method of claim 5, whereinmodifying the generic 3D mesh further by applying point cloud dataoccurs in real time.
 11. The method of claim 5, wherein developing thevirtual testing model specific to the subject occurs without filling allholes and without resolving all discontinuities present in the pointcloud data representing the subject's outer surface.
 12. The method ofclaim 5, wherein the subject is a human subject, the human subject issubjected to a motion capture session while emulating an activity, andwherein, concurrent with the motion capture session, the imaging of thesubject is completed, thereby allowing the adapting of the generic 3Dmesh to be completed in real time to develop the virtual testing modelof the human subject in motion.
 13. The method of claim 5, furthercomprising animating the virtual testing model with the computer basedon motion capture data of the subject in motion and subjecting theanimated virtual testing model to simulated aerodynamic testing.
 14. Themethod of claim 13, wherein the subject is a human subject, furthercomprising supplementing the animated virtual testing model of the humansubject with an accessory submodel for use by the computer, theaccessory submodel representing at least one of an article of clothingworn by the human subject or an item of equipment used by the humansubject.
 15. The method of claim 14, wherein the accessory submodelcomprises a submodel for a garment wearable by the human subject. 16.The method of claim 14, wherein the accessory submodel comprises asubmodel for a helmet wearable by the human subject.
 17. The method ofclaim 14, wherein the accessory submodel comprises a submodel for abicycle to be ridden by the human subject.
 18. The method of claim 13,further comprising displaying the animated virtual testing model, anddynamically evaluating changes to the human subject based on changes tothe animated virtual testing model made via the computer.
 19. A methodfor developing a virtual testing model of a human subject for use insimulated testing using one or more computers, comprising: using acomputer to generate a simulation environment for a user in which ageneric 3D mesh of the human subject is calculated; receiving a computerinput identifying a height of the human subject and at least onereference point on the human subject; receiving a computer input ofpoint cloud data developed from imaging the human subject with an imagescanner; receiving a computer input of skeletal structure and jointlocations; representing at least the human subject's outer surface inthe user in the simulation environment; using the computer to adapt thegeneric 3D mesh to the human subject by modifying the generic 3D mesh tohave a corresponding height and at least one corresponding referencepoint, the corresponding height being calculated or numerically sampledto correspond to the identified height of the subject and the at leastone corresponding reference point being calculated or numericallysampled to correspond to the identified at least one reference point ofthe subject; using the computer to modify the generic 3D mesh further byapplying at least a portion of the point cloud data from the imagedsubject's outer surface at selected locations to scale the generic 3Dmesh to correspond to the subject, wherein modifying the generic 3D meshcomprises iteratively: correlating a thin segment of point cloud datadefined to extend perpendicular to a desired axis with a correspondinglocation in the generic 3D mesh, identifying valid data points in thethin segment of point cloud data with two-coordinate references in aplane extending perpendicular to the desired axis, using thetwo-coordinate references of the valid data points to calculate acircumference among the valid data points within the thin segment;discarding all data points and retaining the calculated circumference,at the corresponding location in the generic 3D mesh, resizing thegeneric 3D mesh to have the calculated circumference, wherein if novalid point cloud data exists for any thin segment and a circumferencecannot be calculated, then a generic circumference at the correspondinglocation in the generic 3D mesh is retained, wherein a resultingmodified generic 3D mesh having calculated circumferences atcorresponding locations more closely corresponds to the human subjectsactual outer surface than the generic 3D mesh, the modified generic 3Dmesh thereby defining a virtual testing model specific to the humansubject that provides increased accuracy in testing.
 20. Anon-transitory computer-readable storage medium storing a computerprogram product, the computer program product including instructions,the instructions when executed, instructing one or more processors toperform a method, the method comprising: receiving an input via acomputer of motion capture data of a human subject in motion in a motioncapture session using a generic 3D mesh of the human subject; concurrentwith the motion capture session, imaging at least a portion of the humansubject with point cloud data of the human subject; and in real time,adapting the generic 3D mesh to match the human subject more closely bycorrelating thin segments of the point cloud data along a desired axisto corresponding locations in the generic 3D mesh and applying validpoint cloud data from the segments to selectively resize the generic 3Dmesh to more closely match the human subject at locations where validpoint cloud data is present, and if no valid point cloud data exists fora specific segment, then retaining dimensions of the generic mesh at alocation corresponding to the specific segment; and displaying an imageof the human subject in simulated aerodynamic testing showing a dragweight per pixel.